code: def test_missing_values(sparse_data):
      X, y, categorical = sparse_data
      mf = meta_features.helper_functions["MissingValues"](
          X, y, logging.getLogger("Meta"), categorical
      )
      assert sparse.issparse(mf.value)
      assert mf.value.shape == X.shape
      assert mf.value.dtype == bool
      assert 0 == np.sum(mf.value.data)
  
imports: import logging
  import os
  import arff
  import numpy
  from scipy import sparse
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import StandardScaler
  import autosklearn.metalearning.metafeatures.metafeatures
  from autosklearn.pipeline.components.data_preprocessing.feature_type import FeatTypeSplit
  import pytest
label_desc: Use image node_id 3 for calling a global function with example usage: test_missing_values(sparse_data) without return types